Statistical Modeling of Temperatures in Iraq Under a Fuzzy Environment

Abstract

In this article, we modeling a set of data which represent the temperatures per day in the governorates of Iraq, which were taken from the General Authority for Meteorology and Seismic Monitoring for the year (2021) under variety statistical models, namely, linear, logarithmic, inverse, quadratic, cubic, compound , S, power, Growth , exponential, and logistic model by using classical principle and fuzzy principle by building a fuzzy information system under vary values of Alfa-cuts to generate membership values to the set of Temperatures to obtain a classical set that takes into account the inaccuracy in data collection , significance of the models was testing by the probabilistic value Sig. to reach to the best model that represents the data of Temperatures , we are compare among them by using mean square error (MSE). We are concluded that the use of the principle of fuzziness in the fitting of the models led to an increase in the accuracy of these models, and the mean squares error (MSE) for all the models that have been fitted is reduced on whether the data are traditional. We are also note that the best model in representing the Temperatures data with is the is power model to having it the lowest (MSE) among all the models, followed by the, Compound, Exponential Growth models at all